Abstract
The quadratic discriminant analysis (XQDA) method learns a general projection matrix for all cameras with its strong generalization ability, but it ignores the inherent properties of each camera itself and does not take feature of changes in each camera into account, causing each person under the camera to have a certain feature distortion problem which makes its discriminative ability worse. In this paper, feature augmentation is used to enhance the inherent properties of each camera. By ensuring the generalization ability of the camera, the feature of changes within each camera is taken into consideration and the final discriminative ability is improved. Finally, experiments on a challenging person re-identification dataset, VIPeR, show that the proposed method outperforms the state-of-the-art methods.
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References
Serikawa, S., & Lu, H. (2014). Underwater image dehazing using joint trilateral filter. Oxford: Pergamon Press.
Lu, H., Li, Y., Uemura, T., et al. (2018). Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Generation Computer Systems, 82, 142–148.
Xu, X., He, L., Lu, H., et al. (2019). Deep adversarial metric learning for cross-modal retrieval. World Wide Web, 22(2), 657–672.
Bazzani, L., Cristani, M., & Murino, V. (2013). Symmetry-driven accumulation of local features for human characterization and re-identification. Computer Vision & Image Understanding, 117(2), 130–144.
Ma, B., & Su, Y. (2012). Local descriptors encoded by fisher vectors for person re-identification. In International conference on computer vision (pp. 413–422). Berlin: Springer.
Zhao, R., Ouyang, W., & Wang, X. (2014). Person re-identification by salience matching. In IEEE International Conference on Computer Vision (pp. 2528–2535).
Zhao, R., Ouyang, W., & Wang, X. (2014). Learning mid-level filters for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 144–151). IEEE.
Liao, S., & Li, S. Z. (2015). Efficient PSD constrained asymmetric metric learning for person re-identification. In IEEE International Conference on Computer Vision (pp. 3685–3693). IEEE.
Zhang, L., Xiang, T., & Gong, S. (2016). Learning a discriminative null space for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 1239–1248). IEEE.
Chen, D., Yuan, Z., Chen, B., et al. (2016). Similarity learning with spatial constraints for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 1268–1277). IEEE.
Wang, J., Zhu, J., Wang, Z., et al. (2016). Contextual similarity regularized metric learning for person re-identification. In International Conference on Pattern Recognition (pp. 2048–2053).
Kodirov, E., Xiang, T., & Gong, S. (2015). Dictionary learning with iterative Laplacian regularization for unsupervised person re-identification. In British Machine Vision Conference (pp. 44.1–44.12).
Yang, X., Wang, M., Hong, R., et al. (2017). Enhancing person re-identification in a self-trained subspace. ACM Transactions on Multimedia Computing Communications & Applications, 13(3), 27.
Hirzer, M. (2012). Large scale metric learning from equivalence constraints. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 2288–2295). IEEE Computer Society.
Li, Z., Chang, S., Liang, F., et al. Learning locally-adaptive decision functions for person verification. In Computer Vision and Pattern Recognition (pp. 3610–3617). IEEE.
Liao, S., Hu, Y., Zhu, X., et al. (2015). Person re-identification by local maximal occurrence representation and metric learning. In Computer Vision and Pattern Recognition (pp. 2197–2206). IEEE.
Pedagadi, S., Orwell, J., Velastin, S., et al. (2013). Local fisher discriminant analysis for pedestrian re-identification. In Computer Vision and Pattern Recognition (pp. 3318–3325). IEEE.
An, L., Yang, S., & Bhanu, B. (2015). Person re-identification by robust canonical correlation analysis. IEEE Signal Processing Letters, 22(8), 1103–1107.
An, L., Kafai, M., Yang, S., et al. (2016). Person re-identification with reference descriptor. IEEE Transactions on Circuits & Systems for Video Technology, 26(4), 776–787.
Lisanti, G., Masi, I., & Bimbo, A. D. (2014). Matching people across camera views using kernel canonical correlation analysis. In Proceedings of the International Conference on Distributed Smart Cameras (pp. 1–6). ACM.
Chen, Y. C., Zheng, W. S., Lai, J. H., et al. (2017). An asymmetric distance model for cross-view feature mapping in person re-identification. IEEE Transactions on Circuits & Systems for Video Technology, 27(8), 1661–1675.
Chen, Y. C., Zheng, W. S., Lai, J. (2015). Mirror representation for modeling view-specific transform in person re identification. In International Conference on Artificial Intelligence (pp. 3402–3408). AAAI Press.
Hamm, J., & Lee, D. D. (2008). Grassmann discriminant analysis: a unifying view on subspace-based learning. In Proceedings of the 25th International Conference on Machine Learning (pp. 376–383). ACM.
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China under Grant no. 61402237, 61502245, 61772568 and the Natural Science Foundation of Jiangsu Province under Grant no. BK20150849. Guangwei Gao is the corresponding author.
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Wang, C., Qi, H., Gao, G., Jing, X. (2020). Quadratic Discriminant Analysis Metric Learning Based on Feature Augmentation for Person Re-Identification. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_18
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DOI: https://doi.org/10.1007/978-3-030-17763-8_18
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